1. Technical Field of the Invention
This invention relates generally to the field of event or object detection. More specifically, the present invention relates to methods for event or object detection where the false alarm rate of detection can be refined through both machine learning and operator feedback.
2. Background
It is generally desired to reduce the amount of downtime in industrial processes. Downtime is caused by a number of factors and it is desirable to identify those components of a particular process that can be indicators of an upcoming failure. These indicators can then be detected by an automated computer system in order to provide a warning of an event. This approach can be extended to generalized detection of an object of interest.
Industrial processes can be dynamic environments. Variability in operating conditions in industrial processes has the potential to cause loss of production, damaged equipment, and could create an unsafe operating environment. When an upset condition occurs, the operators are inundated with huge amounts of data to be processed in a short amount of time. Automated computer systems which monitor and study processes during production can be valuable tools for the operators. Such systems could not only advise the operators of the various actions to be taken to keep the processes running in a stable fashion, they could also minimize the probability of downtimes.
In order to manage the dynamics of an industrial process, complex process control systems have been widely established. In a paper mill, for example, the number of I/O connections in typical mills can vary from 30,000 to more than 100,000. The industry is constantly searching for ways to manage these complex systems in better ways. The first issue to be addressed is in how to handle the huge amount of raw sensor data available within the system. High-dimensional data analysis and reduction are important techniques used to help reduce the dimensionality of the huge amount of raw data (1). From here, various process monitoring and simulation methods exist. These methods are typically either data driven, analytical, or knowledge based (2). Sometimes, combining existing methods proves to be beneficial.
Various techniques from modeling and simulation attempt to characterize the process behavior and are used to develop models for predicting how the system will respond during system upsets or equipment changes. Some research has shown that even small fluctuations in process signals may be precursors to predicting system upsets (2). It is important for an automated system to be able to distinguish the inherent variability of the process from the precursors to system upsets or faults. One of the biggest challenges in a system which has a great deal of inherent variability is to identify when, where, and how much change is significant. If a system cannot correctly identify the precursors to a failure state, the operators may be inundated with false alarms and lose faith in the reliability of the automated system.
For most minor process fluctuations the process controllers (Proportional, Integral, and Derivative (PID)) and model predictive controllers are designed to maintain satisfactory operations by compensating for the effects of disturbances and changes occurring in the process. However, there are some changes in the process which cause disturbances which the controllers cannot adequately handle. These are the disturbances that may lead to faults (3) (4).
Isermann (5) wrote a review article on fault detection based on modeling and system estimation. He claims that with a good model of the process, we can improve our ability to indicate when process faults are likely. As with other similar process models, his system compared current process signatures and outputs with those from the model. When values above or below some threshold were detected, they were labeled as fault indicators. The problem is that when the system is so complex and dynamic, models like Isermann's are often limited. Systems that are currently available rarely try to evaluate the process, for example, the process performed in a paper mill, from the raw material through final product. More often, they try to break the process up into its subprocesses and in doing so, some dependencies may be overlooked. This is especially important when considering the amount of recirculated material within the system. Due to the interdependencies of the various processes in the system, along with the recirculation of material, tracing the time lags in the system also becomes an enormously challenging problem.
Some research efforts have looked at inducing a model using time-series analysis. One classical approach is to build an autoregressive moving average (ARMA) (6). Often associated with the ARMA approach is the cumulative sum (CUSUM) of the residuals method to identify faults. Unfortunately, these methods are limited when the process has many modes of operation, or grades, which are produced in a single process (7).
Research efforts, by Kim et al., have focused on monitoring various process signatures in real-time and incorporating these with equipment maintenance history data and in-line measurements of product quality (8). Combining information in this way helped build stronger process models.
To add to the overall difficulty of dealing with a complex system, uncertainty exists in the sensory measurements, there is cooperation among certain sensors, and there are competing objectives among other sensors. Basir et al. (3) presented a probabilistic approach for modeling the uncertainty and cooperation between sensors. Their research shows how measures of variation can be used to capture both the quality of sensory data and the interdependent relationships that might exist between the different sensors. Some methods presented in this work use information about the variance and standard deviation of each sensor to capture similar relationships in the process.
Both within the paper industry, as well as in other manufacturing environments, various research efforts (9) (10) have explored using neural networks to model the process dynamics. Some research has demonstrated the ability of time-delay neural networks to capture the dynamics of the process. Others (11) (8) have explored the possibility of knowledge based neural network models.
The limitation in using neural network models is that the model may become overtrained for a given set of training inputs. When a neural network becomes overtrained, it has modeled the training set too closely and cannot correctly generalize to other inputs. Therefore, when process conditions change, retraining of the network model may be necessary. While such limitations need to be accounted for, neural network models can still be a very useful tool. This is especially evident when they are paired with other methods, like sensitivity analysis.
A sensitivity analysis indicates which input variables are considered most important by a particular neural network. Sensitivity analysis can give important insights into the usefulness of individual variables (12). In processes where more than one product or grade of product is produced on the same process equipment, there becomes another challenge in dealing with different modes of operation. For this reason, clustering algorithms are useful. Clustering algorithms have the advantage of discovering multiple clusters of operating modes, allowing for the system to create multiple functions to describe modes of good or bad process conditions.